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https://issues.apache.org/jira/browse/HDFS-12051?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16271687#comment-16271687
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Yongjun Zhang commented on HDFS-12051:
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HI [[email protected]],
Thanks for working on this issue.
I did a review and have the following high level comments:
1. The original NameCache works like this, when loading fsimage, it put names
into a transient cache and remember the counts of each name, if the count of a
name reachs a threshold (configurable with default 10), it promote the name to
the permanent cache. After fsimage is loaded, it will clean up the transient
cache and freeze the final cache. The problem described here is about
calculating snapshotdiff which happens after fsimage loading. Thus any new
name, even if it appears many times, would not benefit from the NameCache.
Let's call this solution1, your change is to always allow the cache to be
updated, and let's call it solution2.
2. If we modify solution1 to keep updating the cache instead of freezing it,
we have chance to help the problem to solve here, however, depending on the
threshold, the number of entries in the final cache of solution1 can be very
different, thus memory footprint can be very different.
3. The cache size to be configured in solution2 would impact the final memory
footprint too. If it's configured too small, we might end up many duplicates
too. So having a reasonable default configuration would be important. It's so
internal that we may not easily make good recommendation to users when to
adjust it.
4. How much memory we are saving when saying "8.5% reduction"?
5. "In practice most of the time some names occur much more frequently than
others". Wonder if you have examples from the case you studied, why some Names
appear so much more than others, what patterns the names have? is it an
artifact of snapshot implementation?
6. Solution2 might benefit some cases, but make other cases worse. If we decide
to proceed, wonder if we can make both solution1 and solution2 available, and
make it switchable when needed.
7. Suggest to add more comments in code. For example. {{for (int colsnChainLen
= 0; colsnChainLen < 5; colsnChainLen++) {}}, what this does, and why "5".
Thanks.
> Intern INOdeFileAttributes$SnapshotCopy.name byte[] arrays to save memory
> -------------------------------------------------------------------------
>
> Key: HDFS-12051
> URL: https://issues.apache.org/jira/browse/HDFS-12051
> Project: Hadoop HDFS
> Issue Type: Improvement
> Reporter: Misha Dmitriev
> Assignee: Misha Dmitriev
> Attachments: HDFS-12051.01.patch, HDFS-12051.02.patch
>
>
> When snapshot diff operation is performed in a NameNode that manages several
> million HDFS files/directories, NN needs a lot of memory. Analyzing one heap
> dump with jxray (www.jxray.com), we observed that duplicate byte[] arrays
> result in 6.5% memory overhead, and most of these arrays are referenced by
> {{org.apache.hadoop.hdfs.server.namenode.INodeFileAttributes$SnapshotCopy.name}}
> and {{org.apache.hadoop.hdfs.server.namenode.INodeFile.name}}:
> {code}
> 19. DUPLICATE PRIMITIVE ARRAYS
> Types of duplicate objects:
> Ovhd Num objs Num unique objs Class name
> 3,220,272K (6.5%) 104749528 25760871 byte[]
> ....
> 1,841,485K (3.7%), 53194037 dup arrays (13158094 unique)
> 3510556 of byte[17](112, 97, 114, 116, 45, 109, 45, 48, 48, 48, ...), 2228255
> of byte[8](48, 48, 48, 48, 48, 48, 95, 48), 357439 of byte[17](112, 97, 114,
> 116, 45, 109, 45, 48, 48, 48, ...), 237395 of byte[8](48, 48, 48, 48, 48, 49,
> 95, 48), 227853 of byte[17](112, 97, 114, 116, 45, 109, 45, 48, 48, 48, ...),
> 179193 of byte[17](112, 97, 114, 116, 45, 109, 45, 48, 48, 48, ...), 169487
> of byte[8](48, 48, 48, 48, 48, 50, 95, 48), 145055 of byte[17](112, 97, 114,
> 116, 45, 109, 45, 48, 48, 48, ...), 128134 of byte[8](48, 48, 48, 48, 48, 51,
> 95, 48), 108265 of byte[17](112, 97, 114, 116, 45, 109, 45, 48, 48, 48, ...)
> ... and 45902395 more arrays, of which 13158084 are unique
> <--
> org.apache.hadoop.hdfs.server.namenode.INodeFileAttributes$SnapshotCopy.name
> <-- org.apache.hadoop.hdfs.server.namenode.snapshot.FileDiff.snapshotINode
> <-- {j.u.ArrayList} <--
> org.apache.hadoop.hdfs.server.namenode.snapshot.FileDiffList.diffs <--
> org.apache.hadoop.hdfs.server.namenode.snapshot.FileWithSnapshotFeature.diffs
> <-- org.apache.hadoop.hdfs.server.namenode.INode$Feature[] <--
> org.apache.hadoop.hdfs.server.namenode.INodeFile.features <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlockInfo.bc <-- ... (1
> elements) ... <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap$1.entries <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap.blocks <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlockManager.blocksMap <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlockManager$BlockReportProcessingThread.this$0
> <-- j.l.Thread[] <-- j.l.ThreadGroup.threads <-- j.l.Thread.group <-- Java
> Static: org.apache.hadoop.fs.FileSystem$Statistics.STATS_DATA_CLEANER
> 409,830K (0.8%), 13482787 dup arrays (13260241 unique)
> 430 of byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 353 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 352 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 350 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 342 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 341 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 341 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 340 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 337 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...), 334 of
> byte[32](116, 97, 115, 107, 95, 49, 52, 57, 55, 48, ...)
> ... and 13479257 more arrays, of which 13260231 are unique
> <-- org.apache.hadoop.hdfs.server.namenode.INodeFile.name <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlockInfo.bc <--
> org.apache.hadoop.util.LightWeightGSet$LinkedElement[] <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap$1.entries <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap.blocks <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlockManager.blocksMap <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlockManager$BlockReportProcessingThread.this$0
> <-- j.l.Thread[] <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap$1.entries <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlocksMap.blocks <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlockManager.blocksMap <--
> org.apache.hadoop.hdfs.server.blockmanagement.BlockManager$BlockReportProcessingThread.this$0
> <-- j.l.Thread[] <-- j.l.ThreadGroup.threads <-- j.l.Thread.group <-- Java
> Static: org.apache.hadoop.fs.FileSystem$Statistics.STATS_DATA_CLEANER
> ....
> {code}
> To eliminate this duplication and reclaim memory, we will need to write a
> small class similar to StringInterner, but designed specifically for byte[]
> arrays.
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